EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation

Quantitative microstructural characterization is fundamen-tal to materials science, and electron micrographs (EMs) provide indis-pensable high-resolution insights. However, progress in deep learning-based analysis of EMs has been hampered by the scarcity of large-scale,expert-annotated public datasets. To address this issue, we introduceEM3M, a large-scale and multimodal dataset for instance-level under-standing of EMs. EM3M comprises 5,091 high-quality EMs, approxi-mately 3 million instance segmentation annotations, and image-leveltextual descriptions with disentangled attributes. The dataset is con-structed through a rigorous multi-stage curation and validation pipeline,with comprehensive statistical analyses to ensure reliability and repro-ducibility. Building upon these curated image-text pairs, we further pro-vide a text-to-image diffusion model that serves as a controllable dataaugmentation engine, demonstrating that synthetic augmentation con-sistently improves downstream segmentation performance. To establish asystematic benchmark, we evaluate representative instance segmentationmethods on EM3M. Our results reveal that conventional detection-basedand query-based methods struggle with the extreme instance densitiesand textural complexities inherent in EMs. We additionally provide anoptimized flow-based baseline to facilitate fair comparison and future re-search. EM3M1 , the generative engine2 , and an online demo3 are publiclyavailable to support future research in automated materials analysis.